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ARXIV:2604.06086 · INTERPRETABLE AI · SUBMITTED 08 APR · 03:21 UTC · FRESHNESS UNKNOWN
ARXIV:2604.06086INTERPRETABLE AISUBMITTED 08 APR · 03:21 UTCFRESHNESS UNKNOWNOlexander Mazurets · Olexander Barmak · Leonid Bedratyuk · Iurii Krak · arXiv
LAG-XAI is a geometric framework for interpretable paraphrasing in Transformers, enabling efficient LLM hallucination detection and mechanistic understanding.
Opportunity summary
Pain LAG-XAI is a geometric framework for interpretable paraphrasing in Transformers, enabling efficient LLM hallucination detection and mechanistic understanding.
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LAG-XAI is a geometric framework for interpretable paraphrasing in Transformers, enabling efficient LLM hallucination detection and mechanistic understanding. This paper introduces LAG-XAI (Lie Affine Geometry for Explainable AI), a novel geometric framework that models…
Modern Transformer-based language models achieve strong performance in natural language processing tasks, yet their latent semantic spaces remain largely uninterpretable black boxes. This paper introduces LAG-XAI (Lie Affine Geometry for Explainable AI), a novel…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Modern Transformer-based language models achieve strong performance in natural language processing tasks, yet their latent semantic spaces remain largely uninterpretable black boxes. Code availability…
Interpretable AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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LAG-XAI is a geometric framework for interpretable paraphrasing in Transformers, enabling efficient LLM hallucination detection and mechanistic understanding.
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10.48550/arXiv.2604.06086LAG-XAI is a geometric framework for interpretable paraphrasing in Transformers, enabling efficient LLM hallucination detection and mechanistic understanding.
Abstract
Modern Transformer-based language models achieve strong performance in natural language processing tasks, yet their latent semantic spaces remain largely uninterpretable black boxes. This paper introduces LAG-XAI (Lie Affine Geometry for Explainable AI), a novel geometric framework that models paraphrasing not as discrete word substitutions, but as a structured affine transformation within the embedding space. By conceptualizing paraphrasing as a continuous geometric flow on a semantic manifold, we propose a computationally efficient mean-field approximation, inspired by local Lie group actions. This allows us to decompose paraphrase transitions into geometrically interpretable components: rotation, deformation, and translation. Experiments on the noisy PIT-2015 Twitter corpus, encoded with Sentence-BERT, reveal a "linear transparency" phenomenon. The proposed affine operator achieves an AUC of 0.7713. By normalizing against random chance (AUC 0.5), the model captures approximately 80% of the non-linear baseline's effective classification capacity (AUC 0.8405), offering explicit parametric interpretability in exchange for a marginal drop in absolute accuracy. The model identifies fundamental geometric invariants, including a stable matrix reconfiguration angle (~27.84°) and near-zero deformation, indicating local isometry. Cross-domain generalization is confirmed via direct cross-corpus validation on an independent TURL dataset. Furthermore, the practical utility of LAG-XAI is demonstrated in LLM hallucination detection: using a "cheap geometric check," the model automatically detected 95.3% of factual distortions on the HaluEval dataset by registering deviations beyond the permissible semantic corridor. This approach provides a mathematically grounded, resource-efficient path toward the mechanistic interpretability of Transformers.
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PROBLEM
LAG-XAI is a geometric framework for interpretable paraphrasing in Transformers, enabling efficient LLM hallucination detection and mechanistic understanding. This paper introduces LAG-XAI (Lie Affine Geometry for Explainable AI), a novel geometric framework that models paraphra...
METHOD
Modern Transformer-based language models achieve strong performance in natural language processing tasks, yet their latent semantic spaces remain largely uninterpretable black boxes. This paper introduces LAG-XAI (Lie Affine Geometry for Explainable AI), a novel geometric framew...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Modern Transformer-based language models achieve strong performance in natural language processing tasks, yet their latent semantic spaces remain largely uninterpretable black boxes. Code availability is...
WHY NOW
Interpretable AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
LAG-XAI is a geometric framework for interpretable paraphrasing in Transformers, enabling efficient LLM hallucination detection and mechanistic understanding. This paper introduces LAG-XAI (Lie Affine Geometry for Explainable AI), a novel geometric framework that models paraphrasing not as discrete word substitutions, but as a structured affine transformation within the embedding space.
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partial
Modern Transformer-based language models achieve strong performance in natural language processing tasks, yet their latent semantic spaces remain largely uninterpretable black boxes. This paper introduces LAG-XAI (Lie Affine Geometry for Explainable AI), a novel geometric framework that models paraphrasing not as discrete word substitutions, but as a structured affine transformation within the embedding space.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Modern Transformer-based language models achieve strong performance in natural language processing tasks, yet their latent semantic spaces remain largely uninterpretable black boxes. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Interpretable AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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LAG-XAI is a geometric framework for interpretable paraphrasing in Transformers, enabling efficient LLM hallucination detection and mechanistic understanding.
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